Reduction of classification error rates and monitoring system using an artificial class

ABSTRACT

Systems and methods for enhancing the accuracy of classifying a measurement by providing an artificial class. Seizure prediction systems may employ a classification system including an artificial class and a user interface for signaling uncertainty in classification when a measurement is classified in the artificial class.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a divisional of application Ser. No.11/679,135, filed on Feb. 26, 2007, which claims the benefit of U.S.Provisional Patent Application 60/902,580, filed Feb. 21, 2007, entitled“Methods and Systems for Characterizing and Generating aPatient-Specific Seizure Prediction System,” by Snyder et al. (BNCReference No. 11.00P), the complete disclosure of which is incorporatedherein by reference.

BACKGROUND

A variety of systems are used to measure signals and to process andanalyze the signals and provide indications of potential or actualconditions that may be advantageous or disadvantageous. When a potentialor actual condition is detected, an alert may be provided and/or anaction undertaken.

Such systems may use a classification subsystem to analyze the sampledsignals and make a determination regarding characteristics of thesignals. Classifiers are broadly applicable to data analysis in a widearray of fields such as, for example, medicine (as in the analysis ofphysiological signals, medical image analysis, clinical trial analysis),computer vision (as in optical character recognition, face recognition),data mining (as in retail analysis), and communications (as in errordetection and correction systems, speech recognition, spam filtering).Classification systems generally accept values, which may or may not benumerical, related to some features or characteristics of a situationand produce as an output some label related to the features orcharacteristics. For example, a classifier might take as input detailsabout a subject's salary, age, assets, marital status, outstanding debt,and the like and classify the subject as either an acceptable orunacceptable credit risk. As another example, a medical device systemmight measure electrical signals representative of brain activity andcharacterize the signals as indicative of an inter-ictal (not seizure)condition or a pre-ictal (pre-seizure) condition.

Classification errors can be troublesome. In the case of medical devicesystems, classification errors can lead to false positive or falsenegative indications. Both types of classification errors can besignificant and may lead to unnecessary intervention, failure tointervene appropriately, and/or erroneous outputs to the subject, whichover time, could reduce the value of the medical device system to theuser.

SUMMARY

Systems and methods of reducing the error rate for classifiers and foridentifying and indicating uncertainty in classifier outputs aredisclosed herein. Embodiments are described below that employ thedisclosed systems and methods to improve the accuracy of seizureprediction and detection systems. The embodiments may also be configuredto provide indications to a user in cases of uncertain or unreliableclassification.

A classifier examines a feature vector, having one or more components,and attempts to apply a class label to it. The components of the featurevector are commonly numerical values, but need not be. Classifiers arefirst trained by exposing them to a set of data, such as a set oftraining feature vectors. The training feature vectors may each beassociated with a predefined class label, as in supervised learning, orthe feature vectors may not have associated predefined class labels, asin unsupervised learning. In embodiments using unsupervised learning, aset of classes is constructed to fit the training feature vectors. Forexample, data clustering, such as K-means, possibly combined withBayesian inference, may be used to construct a classification based onclusters identified in the training feature vectors. In order to tune aclassifier to the particular user, the classifier parameters may beadjusted so as to optimize some performance metric such as, for example,minimizing a classification error rate.

However, it may not be possible in some circumstances to identify, apriori, all possible classes or to provide training feature vectorsrepresentative of all possible classes. Conventional classificationstrategies are subject to the limitations of the data that they havebeen configured to recognize. Classifiers can make erroneous decisionswhen exposed to data from an unanticipated class, or data containingnoise or other artifacts. If a conventional classifier is presented witha feature vector that is representative of an unknown, unidentifiedclass, a classification error results because the classifier is forcedto apply one of the known class labels to the input feature vector. Thisoccurs even if the input feature vector is atypical of the appliedclassification.

In some embodiments of seizure prediction systems, physiologicalsignals, generally including electrical signals indicative of brainactivity, are measured and a feature vector representative of one ormore aspects of the measured signals is constructed. A classifier isapplied to the feature vector and a corresponding neurological conditionis associated with the feature vector. In some embodiment, the possibleneurological conditions include ictal, pre-ictal, pro-ictal,inter-ictal, contra-ictal, and post-ictal. The consequences of aclassification error may include a failure to suitably warn a monitoredsubject and/or to appropriately intervene.

An artificial class (sometimes referred to herein as an “other” class),which may be thought of as representing a potentially unknown,unidentified class, is introduced. The addition of the artificial classexplicitly accounts for the occurrence of unanticipated datameasurements that may result from different sources, e.g.,uncharacterized brain states, system noise, measurement artifactsintroduced by non-neural signal sources, and so forth. The artificialclass may reduce false positive rates by ensuring that when a classlabel is assigned, the feature vector being classified is not atypicalof that class. Incorporation of the artificial class into a classifiermay reduce the error rate of the classifier, improve therapy, and/orimprove the types of outputs provided to the subject, as described morefully below. Various embodiments and techniques for constructing theartificial class are described below.

Embodiments of seizure prediction, detection, or monitoring systemshaving classifiers embodying an artificial class are described herein.An improved user interface is disclosed wherein a classificationresulting in the artificial class is used to provide a subject with anindication of uncertainty or unreliability in a predicted result.Actions may be indicated in response to a classification resulting inthe artificial class. In the case of repeated indications related to theartificial class, retraining or reconfiguration of a seizure predictionand detection system may be indicated.

For a further understanding of the nature and advantages of the presentinvention, reference should be made to the following description takenin conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating aspects of feature extractors andclassifiers;

FIG. 2 is a graph of two probability density functions for an exampleclassifier;

FIG. 3 is a flow chart for some embodiments of ways to construct anartificial class;

FIG. 4 is a graph of three probability density functions for anembodiment according to the teachings herein;

FIG. 5 is a graph of three probability density functions for anembodiment according to the teachings herein;

FIG. 6 is a flow chart for a seizure prediction system embodying aspectsof the invention;

FIG. 7 is a simplified diagram of a system for monitoring a subject thatmay be configured in accordance with the systems and methods describedherein;

FIG. 8 is a block diagram of an implanted communication unit that may beused in accordance with the systems and methods described herein;

FIG. 9 is a block diagram of an external data device that may be used inaccordance with the systems and methods described herein;

FIG. 10 is an example timeline for a typical therapeutic regimen for thetreatment of epilepsy;

FIG. 11 is an example timeline for a therapeutic regimen for thetreatment of epilepsy that may be enabled by the system and methodsdescribed herein.

FIG. 12 depicts an example having two-dimensional feature vectorsclassified into two classes, A and B having Gaussian distributions;

FIG. 13 illustrates the introduction of an artificial class, S having alocation parameter equal to the overall mean and a covariance matrixequal to the covariance of the training vectors; and

FIG. 14 illustrates the introduction of an artificial class, S having alocation parameter equal to the overall mean and a covariance matrixequal to the covariance of the training vectors, with a relativelylarger prior probability.

DETAILED DESCRIPTION

Certain specific details are set forth in the following description andfigures to provide an understanding of various embodiments of theinvention. Certain well-known details, associated electronics andmedical devices are not set forth in the following disclosure to avoidunnecessarily obscuring the various embodiments of the invention.Further, those of ordinary skill in the relevant art will understandthat they can practice other embodiments of the invention without one ormore of the details described below. Finally, while various processesare described with reference to steps and sequences in the followingdisclosure, the description is for providing a clear implementation ofparticular embodiments of the invention, and the steps and sequences ofsteps should not be taken as required to practice this invention.

As described in the background, a variety of medical device systems areused to measure physiological signals from a subject and to processthose signals. Although some of the discussion below focuses onmeasuring EEG signals of subjects and subject populations for thedetection and prediction of epileptic seizures, it should be appreciatedthat the invention is not limited to measuring EEG signals or topredicting epileptic seizures. For example, the invention could also beused in systems that measure one or more of a blood pressure, bloodoxygenation from pulse oximetry measurements, temperature of the brainor other portions of the subject, blood flow measurements, ECG/EKG,heart rate signals, respiratory signals, chemical concentrations ofneurotransmitters, chemical concentrations of medications, pH in theblood, or other physiological or biochemical parameters of a subject.

Furthermore, aspects of the invention may be useful for monitoring andassisting in the treatments for a variety of conditions such as sleepapnea and other sleep disorders, migraine headaches, depression,Alzheimer's, Parkinson's Disease, dementia, attention deficit disorder,stroke, eating disorders, addiction, other neurological or psychiatricdisorders, cardiac disease, diabetes, cancer, or the like.

Using epilepsy as an illustrative example, epilepsy is a disorder of thebrain characterized by chronic, recurring seizures and affects anestimated 50 million people worldwide. Seizures are a result ofuncontrolled discharges of electrical activity in the brain. A seizuretypically manifests itself as sudden involuntary, disruptive, and oftendestructive sensory, motor, and cognitive phenomena. Epilepsy is usuallytreated, though not cured, with medication. Surgery may be indicated incases in which seizure focus is identifiable, and the seizure focus isnot located in the eloquent cortex.

A single seizure most often does not cause significant morbidity ormortality, but severe or recurring neurological conditions can result inmajor medical, social, and economic consequences. Epilepsy is more oftendiagnosed in children and young adults. People with uncontrolledepilepsy are often significantly limited in their ability to work inmany industries and may not be able to legally drive an automobile.

The cause of epilepsy is often uncertain. Symptomatic epilepsies arisedue to some structural or metabolic abnormality of the brain and mayresult from a wide variety of causes including genetic conditions,stroke, head trauma, complications during pregnancy or birth, infectionssuch as bacterial or viral encephalitis, or parasites. Idiopathicepilepsies are those for which no other condition has been implicated asa cause and are often genetic and generalized. In the majority of cases,the cause of a subject's epilepsy is unknown.

One of the most disabling aspects of neurological disorders such asepilepsy is the seeming unpredictability of neurological conditions suchas seizures. Mechanisms underlying the generation of seizures arethought to operate over a period of seconds to minutes before theclinical onset of a seizure. Typically, electrographic manifestations ofa neurological condition are detectible some time before clinicalmanifestations occur. Most work in the quantitative analysis ofneurological conditions has been aimed at detecting these electrographicmanifestations. For example, NeuroPace, Inc. has been developing systemsto detect the electrographic onset of a neurological condition so thatsome action, such as direct electrical stimulation of certain brainstructures, may be taken in an attempt to preempt the clinical onset ofa neurological condition. However, the detection of the electrographiconset of a neurological condition may not come far enough in advance ofthe clinical onset for electrical stimulation or other therapies, suchas the administration of anticonvulsant drugs, to be effective inpreventing the clinical onset. Additionally, seizure activity mayalready be causing harm to the brain before the clinical onset of theseizure.

It is desirable to be able to predict neurological conditions wellbefore their electrographic onset. Embodiments of predictive systemsgenerally comprise a collection of detectors for acquiring data from asubject and an analysis system for processing the data to measure asubject's susceptibility or propensity for a seizure. Predictiveanalysis systems are routinely considered to be comprised ofarrangements of feature extractors and classifiers. Feature extractorsare used to quantify or characterize certain aspects of the measuredinput signals. Classifiers are then used to combine the results obtainedfrom the feature extractors into an overall answer or result. Systemsmay be designed to detect different types of conditions that may bereflective of neural condition. These could include, but are notlimited, to systems designed to detect if the subject's neural conditionis indicative of an increased susceptibility or propensity for aneurological condition or systems designed to detect deviation from anormal condition. As can be appreciated, for other neurological ornon-neurological disorders, the classification of the subject'scondition will be based on systems, feature extractors and classifiersthat are deemed to be relevant to the particular disorder.

FIG. 1 depicts an example of the overall structure of a system forestimating a propensity for the onset of a neurological condition suchas, for example, an epileptic seizure. The input data 102 may compriserepresentations of physiological signals obtained from monitoring asubject. The input data may be in the form of analog signal data ordigital signal data that has been converted by way of an analog todigital converter (not shown). The signals may also be amplified,preprocessed, and/or conditioned to filter out spurious signals ornoise. For purposes of simplicity the input data of all of the precedingforms is referred to herein as input data 102.

The input data 102 from the selected physiological signals is suppliedto one or more feature extractors 104 a, 104 b, 105. A feature extractor104 a, 104 b, 105 may be, for example, a set of computer executableinstructions stored on a computer readable medium, or a correspondinginstantiated object or process that executes on a computing device.Certain feature extractors may also be implemented as programmable logicor as circuitry. In general, a feature extractor 104 a, 104 b, 105 canprocess data 102 and identify some characteristic of the data 102. Sucha characteristic of the data is referred to herein as an extractedfeature.

Each feature extractor 104 a, 104 b, 105 may be univariate (operating ona single input data channel), bivariate (operating on two datachannels), or multivariate (operating on multiple data channels). Someexamples of potentially useful characteristics to extract from signalsfor use in determining the subject's propensity for a neurologicalcondition, include but are not limited to, bandwidth limited power(alpha band [8-13 Hz], beta band [13-18 Hz], delta band [0.1-4 Hz],theta band [4-8 Hz], low beta band [12-15 Hz], mid-beta band [15-18 Hz],high beta band [18-30 Hz], gamma band [30-48 Hz], high frequency power[>48 Hz], bands with octave or half-octave spacings, wavelets, etc.),second, third and fourth (and higher) statistical moments of the EEGamplitudes or other features, spectral edge frequency, decorrelationtime, Hjorth mobility (HM), Hjorth complexity (HC), the largest Lyapunovexponent L(max), effective correlation dimension, local flow, entropy,loss of recurrence LR as a measure of non-stationarity, mean phasecoherence, conditional probability, brain dynamics (synchronization ordesynchronization of neural activity, STLmax, T-index, angularfrequency, and entropy), line length calculations, first, second andhigher derivatives of amplitude or other features, integrals,combinations thereof, relationships thereof including ratios anddifferences. Of course, for other neurological conditions, additional oralternative characteristic extractors may be used with the systemsdescribed herein.

The extracted characteristics can be supplied to one or more classifiers106, 107. Like the feature extractors 104 a, 104 b, 105, each classifier106, 107 may be, for example, a set of computer executable instructionsstored on a computer readable medium or a corresponding instantiatedobject or process that executes on a computing device. Certainclassifiers may also be implemented as programmable logic or ascircuitry. In some embodiments, some classifiers may be optionallyapplied or omitted in various circumstances. For example, when theapplication of one or more classifiers 106 is sufficient to estimatethat a propensity for a neurological condition is sufficiently low, thenother classifiers 107 may not be applied to the extractedcharacteristics. If the classifiers 106 indicate a higher propensity fora neurological condition, then additional classifiers 107 may be appliedto the extracted characteristics.

The classifiers 106, 107 analyze one or more of the extractedcharacteristics and possibly other subject dependent parameters toprovide a result 108 that may characterize, for example, a subject'sneural condition. A signal may be generated in response to theclassification. As examples, and not by way of limitation, a data signalrepresentative of the classification may be generated, or some indicatormay serve to communicate information related to the classification to auser. Some examples of classifiers include k-nearest neighbor (“KNN”),linear or non-linear regression, Bayesian, mixture models based onGaussians or other basis functions, neural networks, and support vectormachines (“SVM”). The classifiers 106, 107 may be customized for theindividual subject and may be adapted to use only a subset of thecharacteristics that are most useful for the specific subject. Forexample, the classifier may detect pre-onset characteristics of aneurological condition or characteristics that indicate being in acontra-ictal condition. Additionally, over time, the classifiers 106,107 may be further adapted to the subject, based, for example, in parton the result of previous analyses and may reselect extractedcharacteristics that are used for the specific subject.

As it relates to epilepsy, for example, one implementation of aclassification of neural conditions defined by the classifiers 106, 107may include classes associated with (1) an inter-ictal condition(sometimes referred to as a “normal” condition), (2) a pre-ictalcondition and/or pro-ictal (sometimes referred to as an “abnormal” or“high-susceptibility” condition), (3) an ictal condition (sometimesreferred to as a “seizure” condition), (4) a post-ictal condition(sometimes referred to as a “post-seizure” condition), and (5) acontra-ictal condition (referred to herein as a “protected” condition).The term “pro-ictal” is used herein to refer to a neurological state orcondition characterized by an increased likelihood of transition to anictal state. A pro-ictal state may transition to either an ictal orinter-ictal state. A pro-ictal state that transitions to an ictal stateis also referred to as pre-ictal.

In another embodiment, it may be desirable to have the classifierclassify the subject as being in one of two conditions—a pre-ictalcondition or inter-ictal condition—which could correspond, respectively,to either an elevated or high propensity for a future seizure or a lowpropensity for a future seizure. For ease of reference, the Figures areshown having only two known classes. It should be appreciated, however,that the present invention is applicable to classifiers that have anynumber of classifications.

FIG. 2 depicts the operation of a conventional classifier that makes useof the probability densities of various classes. The graphs depict theprobability density functions (“PDF”) of two classes of data.Measurement feature vectors for this hypothetical classifier consist ofa single numerical value, x, plotted along a horizontal axis 200. ThePDF of a first class, Class A, is plotted as a solid curve 202. The PDFof a second class, Class B, is plotted as a dotted curve 204. Aprocedure for classifying a measurement is to label the measurement withthe class having the greatest PDF value at the measured value x. Forexample, consider a measurement feature vector with a value of x₁=−2.0,indicated by the reference numeral 206. The value of the PDF for Class A202 at x₁=−2.0 is approximately 0.25, while the value of the PDF forClass B 204 is nearly 0.0. Thus, a measurement of x₁=−2.0 would beclassified as Class A.

Consider an application of the strategy to a measurement of x₂=−4.0,indicated by the reference numeral 208. Both PDFs have values near zero,indicating that the value x₂=−4.0 is atypical of either class.Nonetheless, the strategy would classify the measurement as Class Abecause the PDF of Class A is larger. Similarly, a measurement ofx₃=4.5, indicated by the reference numeral 210, would be classified asClass B even though the measurement is atypical of either class.

Finally, consider a measurement of x₄=1.5, indicated by referencenumeral 212. The values of the two PDFs are nearly equal at themeasurement value. Even though both classes are about equally likelythere, the classifier would classify the measurement feature vector ofx₄=1.5 as Class A because its PDF 202 is slightly higher than the PDF ofClass B 204 at the measured value. Because the PDF values are nearlyequal, the chance of misclassification is almost 50%, even though we mayknow a priori that the measurement is associated with one of the twoclasses.

In some embodiments, the various classes are identified with physicalconditions. The classes may be constructed using a training set of dataderived from physical measurements and associated with the variousclasses. For example, in an epilepsy monitoring system, the training setof data may be obtained from measurements of electrical signalsindicative of brain activity. Features are extracted from the measuredsignals and a feature vector with one or more components is determined.An expert reviews the measured signals, and possibly other dataassociated with the measured signals, and identifies the measuredsignals and their derived feature vector as associated with a particularclass. The training set of feature vectors and their class associationsare then used to construct a classifier. In some embodiments, a PDF maybe constructed from each class using the feature vectors associated withthat class. In some other embodiments, the training set of featurevectors themselves may be used by the classifier, such as, for example,in a KNN classifier.

A conventional classifier, such as for example that described above inrelation to FIG. 2, necessarily classifies any given input featurevector in one of the known, predetermined classes. Such a forcedresponse might be acceptable, or even desirable, in some circumstances.For example, if the classifier is incorporated in an automated systemthat must deterministically execute some course of action in response toa classification, then certainty in classification may be required.

A forced classification choice may not be desirable in some systems. Forexample, if the classifier of FIG. 2 is associated with a seizureprediction system, and if Class A is associated with an inter-ictalcondition and Class B is associated with a pre-ictal or pro-ictalcondition, the result of a forced classification could be detrimental. Amisclassification into Class A, in such a system, would label apre-ictal condition as inter-ictal and no warning of a pre-seizurecondition would be given. On the other hand, a misclassification intoClass B would label a possible inter-ictal condition as pre-ictal orpro-ictal, potentially triggering an unnecessary warning and/orintervention such as, for example, the automatic administration ofmedication or the direct electrical stimulation of the subject's brainor particular peripheral nerves, such as, for example, the vagus nerve.

FIG. 3 is a flow chart for embodiments of some ways of constructing anartificial class and enhancing the performance of a classifier. A set oftraining feature vectors is obtained 302. After the statisticalcharacteristics of the training feature vectors are determined, it maybe desirable to determine an artificial class 306. For example,statistical characteristics of the entire set of training vectors,regardless of class associations, may be ascertained and used forconstructing an artificial class. As another example, a uniformprobability distribution may be selected for an artificial class. Ingeneral, any PDF may be employed for the artificial class, with variousPDFs having different advantages or disadvantages with respect tovarious performance metrics.

A classifier using closed-form PDFs for the training feature vectors andthe artificial class may be constructed 308, such as in examplesdescribed below. Alternatively, the determined statisticalcharacteristics for the artificial class can be used to generate a setof artificial vectors associated with the artificial class 310. Aclassifier is then constructed using both the training set of featurevectors and the artificially generated vectors 312. In yet anotheralternative embodiment, a classifier may be constructed using thetraining set of feature vectors and a closed-form PDF for the artificialclass 314.

FIG. 4 depicts an example wherein an artificial class has been added toenhance the classifier of FIG. 2. The graphs of the PDFs of threeclasses of data described by measurement feature vectors comprising asingle numerical value, x, plotted along a horizontal axis 400 aregiven. The first two, Class A whose PDF is indicated by a solid curve402 and Class B whose PDF is indicated by a dotted curve 404, areidentical to the two classes from the example depicted in FIG. 2 and arereferred to as defined or empirical classes. Now, a third, artificialclass has been added. It is labeled Class S and its PDF is indicated bya dashed curve 414. The artificial class may be formed using any of thesteps 308, 312, 314 shown in FIG. 3. In this example, the artificialClass S has been constructed with a normal distribution having a mean(μ) and standard deviation (σ) equal to the population statistics forthe empirical classes, Class A and Class B, combined:

${f_{x}(x)} = {\frac{1}{\sigma\sqrt{2\pi}}{\mathbb{e}}^{- \frac{{({x - \mu})}^{2}}{2\sigma^{2}}}}$

A classifier using the same strategy of the classifier from the exampleof FIG. 2 is applied to measurement feature vectors. That is, ameasurement feature vector is labeled with the class having the greatestPDF value at the measured value x.

At a value of x₁=−2.0, indicated by the reference numeral 406, the valueof the PDF of Class A 402, approximately 0.25, is greater than thevalues for the PDFs of Class S 414, approximately 0.08, and Class B 404,nearly 0.0. Thus, as in the example of FIG. 2, a measurement of x₁=−2would still be classified as Class A.

At values of x₂=−4.0, indicated by the reference numeral 408, andx₃=4.5, indicated by the reference numeral 410, the value of the PDF ofthe artificial Class S 414 is greater than the value of the PDF ofeither empirical class, and so the classifier would label bothmeasurement feature vectors with Class S. The artificial Class S isassociated with uncertainty or unreliability in the classification.Thus, measurement feature vectors with x₂=−4.0 or x₃=4.5, which areatypical of any empirical class, would be identified as “unknown” or“uncertain.”

At the value x₄=1.5, indicated by reference numeral 412, the value ofthe PDF of the artificial Class S 414 is greater than the value of thePDF of either empirical class, and so a measurement feature vector witha value of x₄=1.5 would be labeled with Class S.

It is expected that the use of an artificial class, such as describedabove for example, will improve the reliability of a classifier byreducing the frequency of misclassification. The artificial class maycapture feature vectors that are either atypical of any of the empiricalclasses and associate those feature vectors with the artificial class,thereby identifying them as unknown or uncertain with regard to all ofthe empirical classes. Similarly, the artificial class may capturefeature vectors near the boundaries between two classes where the chanceof misclassification can be large.

If, for example, the classifier of FIG. 4 is associated with a seizureprediction system, and if Class A is associated with an inter-ictalcondition and Class B is associated with a pre-ictal condition, Class Swould be associated with an “unknown or uncertain” neurologicalcondition.

The measurement feature vector x₁ was assigned the label of Class A,just as in the example of FIG. 2. This is reasonable since x₁ is typicalof Class A and highly atypical of Class B. In this case, the seizureprediction and detection system would indicate a “normal” condition.

The measurement feature vector x₂ was assigned the label of Class S bythe enhanced classifier of FIG. 4, whereas x₂ was assigned the label ofClass A by the classifier of FIG. 2. Thus, the classifier of FIG. 2would characterize the measurement feature vector x₂ as representing aninter-ictal state and the system would indicate a “normal” condition,despite the fact that x₂ is highly atypical of an inter-ictal state. Asystem employing the enhanced classifier of FIG. 4 would insteadindicate an “unknown or uncertain” condition, and a user wouldconsequently be warned of the possibility that the measurement featurevector had been particularly susceptible to misclassification. The user,thus alerted, could then take precautions commensurate with thatpossibility. Such precautions might possibly stop short of theadministration of medication or direct electrical stimulation ofparticular nerves. Instead, for example, the user might be warned tocease activities, such as driving or operating machinery that might behazardous should a seizure ensue. Similarly, a user would be warned thatmonitoring results having measurement vectors x₃ or x₄ result in anunknown or uncertain classification.

In the example of FIG. 4, some statistical qualities, in this case themean and standard deviation, of the entire population of empiricalmeasurement feature vectors may be used to construct the artificialclass. In a sense, this represents the notion that if an undetectable,unrecognized class is present in the data, then any empirical data pointcould represent a member of the unknown class.

FIG. 5 depicts an example wherein a uniform distribution is used tocreate the artificial class. The PDFs of two empirical classes 502 504and an artificial class 514 are illustrated. The artificial class hasbeen constructed with a uniform distribution, and thus its PDF 514 isconstant. Measurement feature vector x₁ 506 will be labeled with ClassA, while measurement feature vectors x₂ 508, x₃ 510, and x₄ 512 will belabeled with Class S, just as in the example of FIG. 4. The choice ofparticular probability distribution may affect the boundaries where theclassifiers change classes as the measurement feature vector valuesvary. However, in each case the addition of the artificial class enablesthe classifier to identify some situations where the classification maybe uncertain or unreliable.

The examples given above used a one-dimensional measurement featurevector primarily for simplicity of exposition. The teachings herein arebroadly applicable to systems employing multi-dimensional featurevectors. In general, any probability distribution may be used togenerate the artificial class. Various PDFs may have differentadvantages or disadvantages with respect to particular performancemetrics. The classification methodology may also be modified in avariety of ways, for example by the consideration of prior probabilitiessuch as used in Bayesian classifier.

In some embodiments, it may be desirable to have only a singleidentified class associated with the set of training vectors, forexample as in a novelty detector. As an example, a training set ofvectors may be measured from a subject during a period in which thesubject's neurological condition is classified as normal. Theintroduction of an artificial class will facilitate the classificationof subsequently measured feature vectors as “not normal” or “unknown”and may be used to determine deviation from normal.

Additional alternative methods of realizing an artificial class arecontemplated by the disclosure herein. For example, an artificial dataset, having specified properties, may be generated for use by aclassifier. Techniques for generating an artificial data set, forexample using random number generation, are known. For example, randomnumbers with a specified distribution can be generated by inversion ofthe cumulative distribution function applied to random numbers from auniform distribution on the unit interval (0,1). The artificial data isthen associated with the artificial class and a classifier is trainedusing both the empirical and artificial data. Other distributionfunctions suitable for generating an artificial data set include thegroup of radial basis functions, e.g. Gaussian, multi quadratic, andthin plate spline. Radial basis functions depend on the distance of anobservation from a location parameter, where the distance may begeometric (e.g. Euclidean or L² norm), statistical (e.g. Mahalanobis),or any other suitable distance metric, e.g. L¹ norm or other p-norm.Other more general distribution functions may be generated to addressspecific requirements of a classifier. For example, error rates for aclassifier that does not utilize an artificial class can be reduced bycharacterizing its error rate as a function of position within thefeature space. A PDF for an artificial class that is proportional to thecharacterized error rate can then be constructed. In this manner,observations in error-prone regions of the feature space will beassigned to the artificial class rather than to a defined or empiricalclass.

As an example, consider a basic k-nearest neighbor (“KNN”) classifier.In a KNN classifier, an input feature vector is compared with featurevectors from a training set that have each been identified with anempirical class. The k nearest (according to a distance metric such as,for example the Euclidean or L² norm) feature vectors from the trainingset are identified. The value of k is a parameter of the classifier andthe best choices for k may depend on the data. Given an input featurevector, its k nearest neighbors from the training set are identified andthe input feature vector is associated with the class having thegreatest number among those nearest neighbors. Additionally, aprobability of class membership may be calculated for each classaccording to its proportion among the k nearest neighbors.Alternatively, the importance of each of the k-nearest neighbors can beweighted according to its distance from the input feature vector using aradial basis function, so as to assign greater weight to short distancesthan to long distances.

One technique for enhancing a KNN classifier with the addition of anartificial class is to model an artificial class as a multi-dimensionalGaussian characterized by the overall group statistics of the empiricalclasses. Such a characterization could use the full covariance matrixfor the empirical classes, or could assume that the components of thefeature vectors in the artificial class are independent and use only adiagonal covariance matrix. Random artificial feature vectors aregenerated from the Gaussian model and associated with the artificialclass. An input feature vector is classified by identifying its knearest neighbors among both the training feature vectors and theartificially generated feature vectors. If the artificial class is mostnumerous among those nearest neighbors, then the input feature vector isassociated with the artificial class.

A second technique for enhancing a KNN classifier with the addition ofan artificial class does not entail generating artificial featurevectors. Instead, a probability density function ƒ₀ for the featurevectors in a training set as a whole is estimated and associated with anartificial class. For example, ƒ₀ could be chosen as a multivariateGaussian density function with covariance matrix, Σ, equal to that ofthe complete set of training vectors.

${f_{0}(x)} = {\frac{1}{\left( {2\pi} \right)^{d/2}{\Sigma }^{1/2}}{\exp\left\lbrack {{- \frac{1}{2}}\left( {x - \mu} \right)^{T}{\Sigma^{- 1}\left( {x - \mu} \right)}} \right\rbrack}}$

Given an input feature vector, its k nearest neighbors among thetraining feature vectors are identified. A (possibly multi-dimensional)volume containing those nearest neighbors is estimated. For example, thevolume of a hypersphere having a radius R equal to the distance from theinput feature vector the k^(th) nearest neighboring vector from thetraining set could be used.

$V = \frac{\pi^{d/2}R^{d}}{\Gamma\left( {{d/2} + 1} \right)}$where d is the number of dimensions of the feature vectors. One possiblevariation is to instead use a hypersphere having a radius, R, halfwaybetween the distance to the k^(th) nearest neighboring training vector,R_(k), and the distance to the (k+1)^(st) nearest neighboring trainingvectors, R_(k+1), that is, R=(R_(k)+R_(k+1))/2. As another variation, avolume halfway between those of the hypersphere out to the k^(th)nearest and (k+1)^(st) nearest neighbors could be used.

$V = {\frac{\pi^{d/2}}{\Gamma\left( {{d/2} + 1} \right)}\frac{\left( {R_{k}^{d} + R_{k + 1}^{d}} \right)}{2}}$The estimated volume is used together with the probability densityassociated with the artificial class at the input feature vector toestimate the number of artificial feature vectors that would be expectedwithin the computed volume, k₀=ƒ₀ V N_(o) where N_(o) represents thetotal number of artificial feature vectors in the feature space. N_(o)is a parameter that may be selected.

As another example, consider the application of a Bayes classifier to aGaussian mixture model having n empirical classes C₁, C₂, . . . C_(n).Under this model, the posterior probability of membership in a classC_(i) is:

${P\left\lbrack {C_{i}❘\overset{\_}{x}} \right\rbrack} = {\frac{{P\left\lbrack C_{i} \right\rbrack}{P\left\lbrack {\overset{\_}{x}❘C_{i}} \right\rbrack}}{P\left\lbrack \overset{\_}{x} \right\rbrack} = \frac{{P\left\lbrack C_{i} \right\rbrack}{f_{x}\left( {\overset{\_}{x}❘C_{i}} \right)}}{\sum\limits_{j}{{f_{x}\left( {\overset{\_}{x}❘C_{j}} \right)}{P\left\lbrack C_{j} \right\rbrack}}}}$

where P[C_(i)] is the prior probability of class C_(i), commonly takenas the proportion of training vectors in the class and C₁. ƒ_(x)(x|C_(i)) is the PDF as a function of feature vector x for class C_(i). ABayes classifier would typically classify x into the class having thegreatest posterior i probability. An artificial class can be added, andmay, for example, be represented as a Gaussian cluster having locationparameter (for example, centroid or mean) equal to the overall mean ofthe training vectors and a covariance matrix equal to the covariance ofthe training vectors. The extent of the influence of the artificialclass on the classifier can be adjusted by the choice of a priorprobability for the artificial class. In general, as the priorprobability of the artificial class is increased, a higher proportion ofclassifications will be made to the artificial class.

The effect of introducing an artificial class and adjusting its priorprobability may be illustrated by the examples of FIGS. 12, 13, and 14.FIG. 12 depicts an example having two-dimensional feature vectorsclassified into two classes, A and B having Gaussian distributions. Thelocation parameter 1202 and 95% ellipse 1206 for a class A, and thelocation parameter 1204 and 95% ellipse 1208 for a class B are shown. Adecision boundary 1210 separates the space of feature vectors into tworegions. Feature vectors to the left of the decision boundary areclassified into class A, and feature vectors to the right of thedecision boundary are classified into class B.

FIG. 13 illustrates the effect of introducing an artificial class, S,having a location parameter equal to the overall mean and a covariancematrix equal to the covariance of the training vectors. In this example,the artificial class is assigned a relatively small prior probability.The location parameter 1302 and 95% ellipse 1306 for a class A, and thelocation parameter 1304 and 95% ellipse 1308 for a class B are shown. Adecision boundary 1310 separates the space of feature vectors into fourregions. Feature vectors in the region labeled “A” are classified intoclass A, and feature vectors in the region labeled “B” are classifiedinto class B. Feature vectors in the region labeled “S₁” or in theregion labeled “S₂” are classified into the artificial class S.

FIG. 14 illustrates the effect of introducing an artificial class, S,having a location parameter equal to the overall mean and a covariancematrix equal to the covariance of the training vectors, with arelatively larger prior probability. The location parameter 1402 and 95%ellipse 1406 for a class A, and the location parameter 1404 and 95%ellipse 1408 for a class B are shown. In this example, two decisionboundaries 1410, 1412 separate the space of feature vectors into threeregions. Feature vectors in the region labeled “A” arc classified intoclass A, and feature vectors in the region labeled “B” are classifiedinto class B. Feature vectors in the region labeled “S” are classifiedinto the artificial class S. Generally, as the prior probability of theartificial class is increased, more and more feature vectors will beclassified into the artificial class. Because the feature vectors thatget classified into the artificial class tend to be pulled from featurevectors having a low probability of membership in the other classes, theoverall accuracy of classification should be improved.

In general, the choice of classifier type and the techniques used forconstructing the artificial class are design implementation details thatmay be chosen with consideration of the particular details of thesituation.

FIG. 6 is a flow chart for an embodiment of a seizure prediction systemthat embodies aspects of the invention. One or more physiologicalsignals from a subject are measured 602. A feature vector is determined604 from the measured signals, for example by a feature extractordescribed above. A classifier, employing an artificial class inaccordance with the invention is applied to classify the feature vector606. The result of the classification may be any of a number of definedclasses associated with neurological states, such as contra-ictal 607,inter-ictal 608, pre-ictal 610, ictal 612, and post-ictal 614, or may bethe artificial class associated with an unknown state 616. Theclassification may be used to generate a signal or to perform someaction. The generation of the signal may cause storage of theclassification in memory, activation of a user output, initiation of atherapy, or the like.

In one particular embodiment, the result of the classification may besignaled to a user 617, 618, 620, 622, 625, 626. Depending on thesignal, the user may be informed that an indication of being “safe” isoutput 627, that no response is required 628, that intervention, such asfor example medication or direct nerve stimulation, is recommended 630,an audible alarm may be issued to alert a caretaker 632, that noresponse is required 634, or, if the classification resulted in theartificial class, to take precautions 636, such as for example notdriving or engaging in other potentially hazardous activities. In eachcase, the system may return to monitoring 638.

If monitoring results in recurrent classifications in the unknown class640, the user may be advised that the system should be checked 642.Recurrent unknown results may be an indication that system parametersneed adjustment, that retraining is desirable, or that some component ofthe system is malfunctioning.

Typically, systems such as those disclosed herein are able to store EEGsignals from the subject. The stored EEG signals may thereafter be usedto improve the classification provided by the classifier. Hence, overtime, the classifier will be exposed to a larger number of featurevectors and will be able to better classify the subject. Thus, it iscontemplated that the various classes will become more robust, while theartificial class would be reduced.

FIG. 7 illustrates a system in which aspects of the invention may beembodied. The system 700 is used to monitor a subject 702 for purposesof measuring physiological signals and predicting neurologicalconditions. The system 700 of the embodiment provides for substantiallycontinuous sampling of brain wave electrical signals such as inelectroencephalograms or electrocorticograms, referred to collectivelyas EEGs.

The system 700 comprises one or more sensors 704 configured to measuresignals from the subject 702. The sensors 704 may be located anywhere onthe subject. In the exemplary embodiment, the sensors 704 are configuredto sample electrical activity from the subject's brain, such as EEGsignals. The sensors 704 may be attached to the surface of the subject'sbody (e.g., scalp electrodes), attached to the head (e.g., subcutaneouselectrodes, bone screw electrodes, and the like), or, preferably, may beimplanted intracranially in the subject 702. In one embodiment, one ormore of the sensors 704 will be implanted adjacent a previouslyidentified epileptic focus, a portion of the brain where such a focus isbelieved to be located, or adjacent a portion of a seizure network.

Any number of sensors 704 may be employed, but the sensors 704 willtypically include between 1 sensor and 32 sensors, and preferablybetween about 4 sensors and about 16 sensors. The sensors may take avariety of forms. In one embodiment, the sensors comprise gridelectrodes, strip electrodes and/or depth electrodes which may bepermanently implanted through burr holes in the head. Exact positioningof the sensors will usually depend on the desired type of measurement.In addition to measuring brain activity, other sensors (not shown) maybe employed to measure other physiological signals from the subject 702.

In an embodiment, the sensors 704 will be configured to substantiallycontinuously sample the brain activity of the groups of neurons in theimmediate vicinity of the sensors 704. The sensors 704 are electricallyjoined via cables 706 to an implanted communication unit 708. In oneembodiment, the cables 706 and communication unit 708 will be implantedin the subject 702. For example, the communication unit 708 may beimplanted in a subclavicular cavity of the subject 702. In alternativeembodiments, the cables 706 and communication unit 708 may be attachedto the subject 702 externally. In yet other embodiments, the sensors maybe leadless (not shown) and may communicate directly with an externaldata device 710.

In one embodiment, the communication unit 708 is configured tofacilitate the sampling of signals from the sensors 704. Sampling ofbrain activity is typically carried out at a rate above about 200 Hz,and preferably between about 200 Hz and about 1000 Hz, and mostpreferably at about 400 Hz. The sampling rates could be higher or lower,depending on the specific conditions being monitored, the subject 702,and other factors. Each sample of the subject's brain activity istypically encoded using between about 8 bits per sample and about 32bits per sample, and preferably about 16 bits per sample.

In alternative embodiments, the communication unit 708 may be configuredto measure the signals on a non-continuous basis. In such embodiments,signals may be measured periodically or aperiodically.

The external data device 710 is preferably carried external to the bodyof the subject 702. The external data device 710 receives and storessignals, including measured signals and possibly other physiologicalsignals, from the communication unit 708. External data device 710 couldalso receive and store extracted features, classifier outputs, patientinputs, and the like. Communication between the external data device 710and the communication unit 708 may be carried out through wirelesscommunication. The wireless communication link between the external datadevice 710 and the communication unit 708 may provide a one-way ortwo-way communication link for transmitting data. The wirelesscommunication link may be any type of wireless communication link,including but note limited to, a radiofrequency link, an infrared link,an ultrasonic link, inductive link, or the like.

In alternative embodiments, it may be desirable to have a directcommunications link from the external data device 710 to thecommunication unit 708, such as, for example, via an interface devicepositioned below the subject's skin. The interface (not shown) may takethe form of a magnetically attached transducer that would enable powerto be continuously delivered to the communication unit 708 and wouldprovide for relatively higher rates of data transmission. Errordetection and correction methods may be used to help insure theintegrity of transmitted data. If desired, the wireless data signals canbe encrypted prior to transmission to the external data device 710.

FIG. 8 depicts a block diagram of one embodiment of a communication unit800 that may be used with the systems and methods described herein.Energy for the system is supplied by a rechargeable power supply 814.The rechargeable power supply may be a battery, or the like. Therechargeable power supply 814 may also be in communication with atransmit/receive subsystem 816 so as to receive power from outside thebody by inductive coupling, radiofrequency (RF) coupling, and the like.Power supply 814 will generally be used to provide power to the othercomponents of the implantable device. Signals 802 from the sensors 704(FIG. 7) are received by the communication unit 800. The signals may beinitially conditioned by an amplifier 804, a filter 806, and ananalog-to-digital converter 808. A memory module 810 may be provided forstorage of some of the sampled signals prior to transmission via atransmit/receive subsystem 816 and antenna 818 to the external datadevice 710 (FIG. 7). For example, the memory module 810 may be used as abuffer to temporarily store the conditioned signals from the sensors 704(FIG. 7) if there are problems with transmitting data to the externaldata device 710 (FIG. 7), such as may occur if the external data device710 experiences power problems or is out of range of the communicationssystem. The external data device 710 can be configured to communicate awarning signal to the subject in the case of data transmission problemsto inform the subject and allow him or her to correct the problem.

The communication unit 800 may optionally comprise circuitry of adigital or analog or combined digital/analog nature and/or amicroprocessor, referred to herein collectively as “microprocessor” 812,for processing the signals prior to transmission to the external datadevice 710. The microprocessor 812 may execute at least portions of theanalysis as described herein. For example, in some configurations, themicroprocessor 812 may run one or more feature extractors 104 a, 104 b,105 (FIG. 1) that extract characteristics of the measured signal thatare relevant to the purpose of monitoring. Thus, if the system is beingused for diagnosing or monitoring epileptic subjects, the extractedcharacteristics (either alone or in combination with othercharacteristics) may be indicative or predictive of a neurologicalcondition. Once the characteristic(s) are extracted, the microprocessor812 may transmit the extracted characteristic(s) to the external datadevice 710 and/or store the extracted characteristic(s) in memory 810.Because the transmission of the extracted characteristics is likely toinclude less data than the measured signal itself, such a configurationwill likely reduce the bandwidth requirements for the communication linkbetween the communication unit 800 and the external data device 710.

In some configurations, the microprocessor 812 in the communication unit800 may comprise multiple cores and may run the one or more classifiers106, 107 (FIG. 1), possibly on separate cores, as described above withrespect to FIG. 1. The result 108 (FIG. 1) of the classification may becommunicated to the external data device 710.

FIG. 9 provides a schematic diagram of some of the components that maybe included in an external data device 900 which may also include anycombination of conventional components. Signals from the communicationunit 800 are received at an antenna 902 and conveyed to atransmit/receive subsystem 904. The signals received may include, forexample, a raw measured signal, a processed measured signal, extractedcharacteristics from the measured signal, a result from analysissoftware that ran on the implanted microprocessor 812 (FIG. 8), or anycombination thereof.

The received data may thereafter be stored in memory 906, such as a harddrive, RAM, EEPROM, removable flash memory, or the like and/or processedby a single core or multiple core microprocessor, application specificintegrated circuit (ASIC) or other dedicated circuitry of a digital oranalog or combined digital/analog nature, referred to hereincollectively as a “microprocessor” 908. Microprocessor 908 may beconfigured to request that the communication unit 800 perform variouschecks (e.g., sensor impedance checks) or calibrations prior to signalrecording and/or at specified times to ensure the proper functioning ofthe system.

Data may be transmitted from memory 906 to microprocessor 908 where thedata may optionally undergo additional processing. For example, if thetransmitted data is encrypted, it may be decrypted. The microprocessor908 may also comprise one or more filters that filter out low-frequencyor high-frequency artifacts (e.g., muscle movement artifacts, eye-blinkartifacts, chewing, and the like) so as to prevent contamination of themeasured signals.

External data device 900 will typically include a user interface fordisplaying outputs to the subject and for receiving inputs from thesubject. The user interface will typically comprise outputs such asauditory devices (e.g., speakers) visual devices (e.g., LCD display,LEDs, and the like), tactile devices (e.g., vibratory mechanisms), orthe like, and inputs, such as a plurality of buttons, a touch screen,and/or a scroll wheel.

Referring again to FIG. 7, in one embodiment, the external data device710 includes a collection of colored LEDs or lights 712, 714, 716 toindicate to the subject his or her condition. For example, if the resultof classification indicates a safe condition, such as a contra-ictalclassification, a green light 716 may be displayed. If theclassification results in an indication that a seizure may be likely orimminent, a red light 712 may be displayed and the subject is therebywarned to cease potentially hazardous activity and to interveneappropriately, such a through the automatic or manual administration ofmedication. If the classification results in the artificial class,indicating an unreliable or uncertain condition, a yellow light 714 isdisplayed. The subject 702 is thus warned that safety cannot be assuredbecause the current neurological state is unknown. The user is thuscautioned to exercise an appropriate level of care, such as, forexample, avoiding potentially hazardous activities while perhaps notnecessarily intervening such as with medication.

While FIG. 7 shows a simple embodiment that uses three lights, a varietyof other types of outputs may be provided to the patient. For example,it may be desirable to have a larger number of lights that provide afiner grade of the subject's condition. For example, a purple light orother colored light may be used to indicate the unknown class, while theyellow light may be used to indicate an increased propensity for aseizure and the red light may be used to indicate a seizure has beendetected.

In yet other embodiments, the lights may have an even finer grade. Forexample, the lights may include an orange light and a yellow light thatmay be used to indicate different levels of susceptibility for seizure,while the red light may be used to indicate a seizure has been detected.For each of the different levels of susceptibility, the subject'sphysician may prescribe different therapies for treating the subject.

Alternative ways for communicating the classification results to thesubject are also contemplated. For example, various audible and tactilesignals, including possibly voice prompts, may be produced correspondingto some classification results or changes of classification. Appropriatetextual warning messages may be displayed in some circumstances. Theclassification result may be used to trigger an automatic response,perhaps by interfacing with another device, when certain classificationresults are indicated.

Recurring results of classifications in the artificial group, indicatinguncertainty or unreliability in the classification, may indicate aproblem with some component of the device, or that the classifier isunable to accurately classify the subject's condition. In thatsituation, instruction may be provided to the subject to bring thedevice to a clinician for possible testing and adjustment. Modificationsto some of the parameters of the classification system may be attemptedto attempt to improve the classification effectiveness.

The user interface may be adapted to allow the subject to indicate andrecord certain events. For example, the subject may indicate thatmedication has been taken, the dosage, the type of medication, mealintake, sleep, drowsiness, occurrence of an aura, occurrence of aneurological condition, or the like. Such inputs may be used inconjunction with the measured EEG data to improve the analysis andclassification.

The LCD display may be used to output a variety of differentcommunications to the subject including, status of the device (e.g.,memory capacity remaining), battery state of one or more components ofsystem, whether or not the external data device 710 is withincommunication range of the communication unit 708, a warning (e.g., aneurological condition warning), a prediction (e.g., a neurologicalcondition prediction), a recommendation (e.g., “take medicine”), or thelike. It may be desirable to provide an audio output or vibratory outputto the subject in addition to or as an alternative to the visual displayon the LCD.

Referring again to FIG. 9, external data device 900 may also include apower source 914 or other conventional power supply that is incommunication with at least one other component of external data device900. The power source 914 may be rechargeable. If the power source 914is rechargeable, the power source may optionally have an interface forcommunication with a charger 916. While not shown in FIG. 9, externaldata device 900 will typically comprise a clock circuit (e.g.,oscillator and frequency synthesizer) to provide the time base forsynchronizing the external data device 900 and the communication unit800 (FIG. 8).

Referring again to FIG. 7, in a preferred embodiment, most or all of theprocessing of the signals received by the communication unit 708 is donein an external data device 710 that is external to the subject's body.In such embodiments, the communication unit 708 would receive thesignals from subject and may or may not pre-process the signals andtransmit some or all of the measured signals transcutaneously to anexternal data device 710, where the prediction of the neurologicalcondition and possible therapy determination is made. Advantageously,such embodiments reduce the amount of computational processing powerthat needs to be implanted in the subject, thus potentially reducingenergy consumption and increasing battery life. Furthermore, by havingthe processing external to the subject, the judgment or decision makingcomponents of the system may be more easily reprogrammed or customtailored to the subject without having to reprogram the communicationunit 708.

In alternative embodiments, the predictive systems disclosed herein andtreatment systems responsive to the predictive systems may be embodiedin a device that is implanted in the subject's body, external to thesubject's body, or a combination thereof. For example, in one embodimentthe predictive system may be stored in and processed by thecommunication unit 708 that is implanted in the subject's body. Atreatment analysis system, in contrast, may be processed in a processorthat is embodied in an external data device 710 external to thesubject's body. In such embodiments, the subject's propensity forneurological condition characterization (or whatever output is generatedby the predictive system that is predictive of the onset of theneurological condition) is transmitted to the external subjectcommunication assembly, and the external processor performs anyremaining processing to generate and display the output from thepredictive system and communicate this to the subject. Such embodimentshave the benefit of sharing processing power, while reducing thecommunications demands on the communication unit 708. Furthermore,because the treatment system is external to the subject, updating orreprogramming the treatment system may be carried out more easily.

In other embodiments, signals may be processed in a variety of ways inthe communication unit 708 before transmitting data to the external datadevice 710 so as to reduce the total amount of data to be transmitted,thereby reducing the energy demands of the transmit/receive subsystem816 (FIG. 8). Examples include: digitally compressing the signals beforetransmitting them; selecting only a subset of the measured signals fortransmission; selecting a limited segment of time and transmittingsignals only from that time segment; extracting salient characteristicsof the signals, transmitting data representative of thosecharacteristics rather than the signals themselves, and transmittingonly the result of classification. Further processing and analysis ofthe transmitted data may take place in the external data device 710.

In yet other embodiments, it may be possible to perform some of theprediction in the communication unit 708 and some of the prediction inthe external data device 710. For example, one or more characteristicsfrom the one or more signals may be extracted with feature extractors inthe communication unit 708. Some or all of the extracted characteristicsmay be transmitted to the external data device 710 where thecharacteristics may be classified to predict the onset of a neurologicalcondition. If desired, external data device 710 may be customizable tothe individual subject. Consequently, the classifier may be adapted toallow for transmission or receipt of only the characteristics from thecommunication unit 708 that are predictive for that individual subject.Advantageously, by performing feature extraction in the communicationunit 708 and classification in an external device at least two benefitsmay be realized. First, the amount of wireless data transmitted from thecommunication unit 708 to the external data device 710 is reduced(versus transmitting pre-processed data). Second, classification, whichembodies the decision or judgment component, may be easily reprogrammedor custom tailored to the subject without having to reprogram thecommunication unit 708.

In yet another embodiment, feature extraction may be performed externalto the body. Pre-processed signals (e.g., filtered, amplified, convertedto digital) may be transcutaneously transmitted from communication unit708 to the external data device 710 where one or more characteristicsare extracted from the one or more signals with feature extractors. Someor all of the extracted characteristics may be transcutaneouslytransmitted back into the communication unit 708, where a second stageof processing may be performed on the characteristics, such asclassifying of the characteristics (and other signals) to characterizethe subject's propensity for the onset of a future neurologicalcondition. If desired, to improve bandwidth, the classifier may beadapted to allow for transmission or receipt of only the characteristicsfrom the subject communication assembly that are predictive for thatindividual subject. Advantageously, because feature extractors may becomputationally expensive and energy hungry, it may be desirable to havethe feature extractors external to the body, where it is easier toprovide more processing and larger power sources.

More complete descriptions of systems that may be used to embody theconcepts of the present disclosure are described in commonly owned,copending U.S. patent application Ser. Nos. 11/321,897, 11/321,898,11/322,150, all filed on Dec. 28, 2005, the complete disclosures ofwhich are incorporated herein by reference. For energy savings, thesystems may embody some of the energy saving concepts described incommonly owned, copending patent application Ser. Nos. 11/616,788 and11/616,793, filed Dec. 27, 2006, the complete disclosures of which areincorporated herein by reference.

The ability to provide long-term low-power ambulatory measuring ofphysiological signals and prediction of neurological conditions canfacilitate improved treatment regimens for certain neurologicalconditions. FIG. 10 depicts the typical course of treatment for asubject with epilepsy. Because the occurrence of neurological conditions1000 over time has been unpredictable, present medical therapy relies oncontinuous prophylactic administration of anti-epileptic drugs (“AEDs”).Constant doses 1002 of one or more AEDs are administered to a subject atregular time intervals with the objective of maintaining relativelystable levels of the AEDs within the subject. Maximum doses of the AEDsare limited by the side effects of their chronic administration.

Reliable long-term essentially continuously operating neurologicalcondition prediction systems would facilitate acute epilepsy treatment.Therapeutic actions, such as, for example, brain stimulation, peripheralnerve stimulation (e.g., vagus nerve stimulation), cranial nervestimulation (e.g., trigeminal nerve stimulation (“TNS”)), or targetedadministration of AEDs, could be directed by output from a neurologicalcondition prediction system on an acute basis. One such course oftreatment is depicted in FIG. 11, in which acute doses of an AED 1006may be administered when it is determined that the subject is at anincreased susceptibility to a seizure. Optionally, relatively lowerconstant doses 1004 of one or more AEDs may also be administered to asubject at regular time intervals in addition to or as an alternative tothe acute administration of AEDs 1006. Medication doses 1006 areadministered just prior to an imminent neurological condition 1008. Bytargeting the doses 1006 at the appropriate times, neurologicalconditions may be more effectively controlled and potentially eliminated1008, while reducing side effects attendant with the chronicadministration of higher levels of the AEDs.

While the present disclosure has been described in connection withvarious embodiments, illustrated in the various figures, it isunderstood that similar aspects may be used or modifications andadditions may be made to the described aspects of the disclosedembodiments for performing the same function of the present disclosurewithout deviating therefrom. Other equivalent mechanisms to thedescribed aspects are also contemplated by the teachings herein. Forexample, instead of generating an “unknown class,” an equivalentclassifier could utilize mathematical techniques that provide an“adaptive classification threshold.” Such a threshold would achieve thesame result as the “unknown class” without labeling the classifieroutput as “unknown”, but would still achieve similar achievements inclassification accuracy.

Furthermore, while the above focuses on classification for monitoringepileptic patients, the present invention is also applicable to otherfields that analyze data for classification. Such areas include textrecognition, speech recognition, image recognition, radar, targeting,data mining (as in retail analysis), communications (as in errordetection and correction systems, speech recognition, spam filtering),and the like. Therefore, the present disclosure should not be limited toany single aspect, but rather construed in breadth and scope inaccordance with the appended claims.

1. A seizure advisory system comprising a user interface, the userinterface comprising: an output interface configured to provide anindication corresponding to a result of classification of data from asubject; and an output interface configured to provide an indication ofuncertainty when a result of classification of data from a subject isindicative of uncertainty in the classification.
 2. A system as recitedin claim 1, wherein the classification of data from a subject comprisesa plurality of classes corresponding to neurological conditions of thesubject.
 3. A system as recited in claim 1, wherein the classificationof data from a subject comprises training a classifier usingunsupervised learning on a training set of data from a subject.
 4. Asystem as recited in claim 2, wherein the classes corresponding toneurological conditions of the subject comprise a class corresponding toa heightened susceptibility of the subject to a seizure.
 5. A system asrecited in claim 2, wherein the classes corresponding to neurologicalconditions of the subject comprise a class corresponding to a reducedsusceptibility of the subject to a seizure.
 6. A system as recited inclaim 2, further comprising: a body configured to generate a signalindicative of the result of classification of data from the subject. 7.A system as recited in claim 6, wherein the body is a handheld device.8. A system as recited in claim 6, wherein the body is configured to beimplanted in the subject.
 9. A seizure advisory system comprising: atleast one sensor input that receives at least one electrical signalindicative of brain activity; a processing unit configured to determinea measurement feature vector based at least in part on the at least oneelectrical signal; a classifier comprising a plurality of empiricalclasses, each empirical class associated with at least one featurevector from a training set of feature vectors and being associated withan estimated neural condition and an other class independent of eachempirical class; and an output device for communicating informationrelated to a result of applying the classifier to the measurementfeature vector.
 10. A seizure advisory system as recited in claim 9,wherein the output device is configured to: provide a signal indicativeof an estimated neural condition in response to a result of applying theclassifier to the measurement feature vector being a class associatedwith the estimated neural condition; and provide a signal indicative ofuncertainty in estimating a neural condition in response to a result ofapplying the classifier being the other class.
 11. A seizure advisorysystem as recited in claim 10, wherein the output device, is configuredto provide a seizure warning in response to the result of applying theclassifier being a class identified with a pre-ictal condition or aclass identified with a pro-ictal condition.
 12. A seizure advisorysystem as recited in claim 10, wherein the other class is associatedwith a plurality of artificially generated feature vectors having aspecified distribution.